Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data

ISBN-13: 9783540666196 / Angielski / Miękka / 1999 / 425 str.

H. H. Bock; H. H. Bock; Hans-Hermann Bock

Opis

Raymond Bisdorff CRP-GL, Luxembourg The development of the SODAS software based on symbolic data analysis was extensively described in the previous chapters of this book. It was accompanied by a series of benchmark activities involving some official statistical institutes throughout Europe. Partners in these benchmark activities were the National Statistical Institute (INE) of Portugal, the Instituto Vasco de Estadistica Euskal (EUSTAT) from Spain, the Office For National Statistics (ONS) from the United Kingdom, the Inspection Generale de la Securite Sociale (IGSS) from Luxembourg 1 and marginally the University of Athens . The principal goal of these benchmark activities was to demonstrate the usefulness of symbolic data analysis for practical statistical exploitation and analysis of official statistical data. This chapter aims to report briefly on these activities by presenting some signifi- cant insights into practical results obtained by the benchmark partners in using the SODAS software package as described in chapter 14 below.

Spis treści

E. Diday: Symbolic Data Analysis and the SODAS Project: Purpose, History, Perspective.- H.H. Bock: The Classical Data Situation.- H.H. Bock: Symbolic Data.- H.H. Bock, E. Diday: Symbolic Objects.- V. Stéphan, G. Hébrail, Y. Lechevallier: Generation of Symbolic Objects from Relational Databases.- P. Bertrand, F. Goupil: Descriptive Statistics for Symbolic Data.- M. Noirhomme-Fraiture, M. Rouard: Visualizing and Editing Symbolic Objects.- Similarity and Dissimilarity: F. Esposito, D. Malerba, V. Tamma, H.H. Bock: Classical Resemblance Measures.- H.H. Bock: Dissimilarity Measures for Probability Distributions.- F. Esposito, D. Malerba, V. Tamma: Dissimilarity Measures for Symbolic Objects.- F. Esposito, D. Malerba, F. Lisi: Matching Symbolic Objects.- Symbolic Factor Analysis: H.H.Bock: Classical Principal Component Analysis.- A. Chouakria, P. Cazes, E. Diday: Symbolic Principal Component Analysis.- N.C. Lauro, F. Palumbo, R. Verde: Factorial Discriminant Analysis on Symbolic Objects.- Discrimination: Assigning Symbolic Objects to Classes: J. Rasson, S. Lissoir: Classical Methods of Discrimination.- J. Rasson, S. Lissoir: Symbolic Kernel Discriminant Analysis.- E. Périnel, Y. Lechevalier: Symbolic Discrimination Rules.- M. Bravo Llatas, J. Garcia-Santesmases: Segmentation Trees for Stratified Data.- Clustering Methods for Symbolic Objects: M. Chavent, H.H. Bock: Clustering Problem, Clustering Methods for Classical Data.- M. Chavent: Criterion-Based Divisive Clustering for Symbolic Data.- P. Brito: Hierarchical and Pyramidal Clustering with Complete Symbolic Objects.- G. Polaillon: Pyramidal Classification for Interval Data Using Galois Lattice Reduction.- M. Gettler-Summa, C. Pardoux: Symbolic Approaches for Three-way Data.-Illustrative Benchmark Analysis: R. Bisdorff: Introduction.- R. Bisdorff: Professional Careers of Retired Working Persons.- A. Iztueta, P. Calvo: Labour Force Survey.- F. Goupil, M. Touati, E. Diday, R. Moult: Census Data from the Office for National Statistics.- A. Morineau: The SODAS Software Package.